Free Databricks Exam Questions
Databricks Certified Machine Learning Professional
Practice with our comprehensive collection of free Databricks Certified Machine Learning Professional exam questions. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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"I used these questions as preparation for my Databricks Machine Learning Engineer Professional exam in August 2025. The real exam questions felt exactly like the ones shown here, and I passed on my first try."
— Maarten from CertSafari
August 2025
Exam Information
Exam Details
Complete information about the Databricks Certified Machine Learning Engineer Professional certification exam
59 scored multiple-choice questions
120 minutes (2 hours)
USD 200 (plus applicable taxes)
2 years
Online Proctored
Prerequisites: No required prerequisites, but course attendance and 1 year of hands-on experience in Databricks is highly recommended.
Exam Topics & Skills Assessed
Key technologies and domains covered in the Machine Learning Engineer Professional exam
Core Databricks Machine Learning Technologies:
- SparkML - Pipelines, estimators, transformers, model evaluation, batch and streaming inference
- Distributed Training - SparkML and pandas Function APIs/UDFs for scaling training pipelines
- Hyperparameter Tuning - Optuna and Ray for distributed hyperparameter optimization
- Advanced MLflow - Nested runs, custom metrics, parameters, artifacts, real-time feature engineering
- Feature Store - Point-in-time correctness, automated pipelines with Feature Engineering Client, online tables, streaming features, on-demand features
- MLOps - Model lifecycle management, testing strategies, environment architectures
- Databricks Asset Bundles (DABs) - ML assets configuration, model serving endpoints, MLflow experiments, registered models
- Automated Retraining - Workflows triggered by drift detection or performance degradation
- Lakehouse Monitoring - Drift detection, statistical tests, snapshot/time series/inference tables, custom metrics, endpoint health monitoring
- Model Deployment - Blue-green and canary deployment strategies, model rollout management
- Custom Model Serving - PyFunc models, MLflow Deployments SDK, REST API, custom model objects
Exam Sections (3 Main Sections):
- Model Development Using Spark ML - SparkML pipelines, estimators, transformers, model tuning with MLlib, model evaluation, batch and streaming scoring, scaling distributed training, hyperparameter tuning with Optuna and Ray, vertical vs horizontal scaling, parallelization strategies, Pandas Function API
- MLOps - Model lifecycle management, validation testing (unit and integration), environment architectures with Databricks Asset Bundles, automated retraining workflows, drift detection and Lakehouse Monitoring, statistical tests, custom metrics, endpoint health monitoring
- Model Deployment - Deployment strategies (blue-green, canary), model rollout with Databricks Model Serving, custom model serving with PyFunc, REST API and MLflow Deployments SDK
Advanced Skills Tested:
- Designing and implementing enterprise-scale machine learning solutions
- Building scalable ML pipelines with SparkML
- Implementing distributed training and hyperparameter tuning
- Leveraging advanced MLflow features for complex experimentation
- Utilizing Feature Store concepts for automated feature pipelines
- Implementing MLOps practices including testing strategies and environment management
- Designing automated retraining workflows triggered by drift or performance degradation
- Monitoring using Lakehouse Monitoring for drift detection and model performance
- Implementing deployment strategies for high-traffic applications
- Deploying custom models and managing model rollouts
- Evaluating trade-offs between different scaling and parallelization strategies
- Ensuring point-in-time correctness in feature lookups to prevent data leakage
About the Databricks Certified Machine Learning Engineer Professional Certification
The Databricks Certified Machine Learning Engineer Professional certification validates your advanced expertise in designing, implementing, and managing enterprise-scale machine learning solutions using advanced Databricks platform capabilities. This professional-level certification demonstrates mastery of building scalable ML pipelines with SparkML, implementing distributed training and hyperparameter tuning, leveraging advanced MLflow features, and utilizing Feature Store concepts for automated feature pipelines.
The certification evaluates expertise in MLOps practices, including testing strategies, environment management with Databricks Asset Bundles, automated retraining workflows, and monitoring using Lakehouse Monitoring for drift detection. Additionally, it assesses your ability to implement deployment strategies, custom model serving, and model rollout management.
This certification is ideal for experienced machine learning engineers who work extensively with Databricks and need to demonstrate advanced skills in production environments. Successful candidates can expect to handle complex ML engineering challenges, implement production-ready ML systems with comprehensive monitoring, testing, and deployment practices using the full feature set of Databricks.